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Record W4394278905 · doi:10.6084/m9.figshare.6210125

Raw data VR Acceptance

2018· dataset· en· W4394278905 on OpenAlexaboutno aff
Hanne Huygelier, Brenda Schraepen, Raymond van Ee, Vero Vanden Abeele, Céline R. Gillebert

Bibliographic record

VenueFigshare · 2018
Typedataset
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRaw dataComputer scienceVirtual realityComputer graphics (images)Human–computer interaction

Abstract

fetched live from OpenAlex

These files contain the raw data of a study on the attitudes of older adults towards head-mounted immersive virtual reality. Sixty participants completed a scale measuring attitudes (Att) towards head-mounted virtual reality, computer proficiency (CP) and computer self-efficacy (CSE) in a first test session. In addition, the Montreal Cognitive Assessment (MoCA) and the praxis subscale of the Birmingham Cognitive Screen (BCoSPraxis) were administered to participants to measure cognitive status and praxis. Using these data we tested whether initial attitudes depend on age, years of formal education and computer proficiency. In addition, 37 participants were exposed to a first HMD-VR user experience and 22 control participants were exposed to content-matched time-lapse videos. We evaluated whether attitudes towards HMD-VR changed more strongly in the HMD-VR versus the control group. In addition, we also measured the experience of the HMD-VR exposure or time-lapse videos, symptoms of cybersickness (SSQ) after each experience and the tendency to answer in a socially desirable fashion (SDS). Moreover, we also measured the openness personality trait using a short form of the Neuroticism Extraversion Openness Inventory (NEO). The data were collected in 3 test phases: phase 3, 4, 5 represent the participants of the HMD-VR group, while test phase 7 represents the control group. The demographic data (Dem) in the shared dataset were adjusted by removing variables that are unnecessary to replicate our results and that may identify individuals. <br>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.060
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0090.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0870.027

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.153
GPT teacher head0.358
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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